FOUNDATIONS OF GENETIC PROGRAMMING

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This is one of the only books to provide a complete and coherent review of the theory of genetic programming (GP). In doing so, it provides a coherent consolidation of recent work on the theoretical foundations of GP. A concise introduction to GP and genetic algorithms (GA) is followed by a discussion of fitness landscapes and other theoretical approaches to natural and artificial evolution. Having surveyed early approaches to GP theory it presents new exact schema analysis, showing that it applies to GP as well as to the simpler GAs. New results on the potentially infinite number of possible programs are followed by two chapters applying these new techniques.

Artificial Intelligence: Foundations of Computational Agents (3版)

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【簡介】 Introduces concepts at the right level of detail, using straightforward language and minimum mathematics Examines the social and ethical impacts of AI, connecting techniques to real-world benefits and harms Explains state-of-the-art algorithms and examples underlying the theory, demonstrating how concepts are applied Provides two complementary software systems – AIPython and AILog - for experimentation and extension Pedagogical features include examples, end-of-chapter reviews, further reading lists, and exercises 【目錄】 Part I - Agents in the World Chapter 1 - Artificial Intelligence and Agents Chapter 2 - Agent Architectures and Hierarchical Control Part II - Reasoning and Planning with Certainty Chapter 3 - Searching for Solutions Chapter 4 - Reasoning with Constraints Chapter 5 - Propositions and Inference Chapter 6 - Deterministic Planning Part III - Learning and Reasoning with Uncertainty Chapter 7 - Supervised Machine Learning Chapter 8 - Neural Networks and Deep Learning Chapter 9 - Reasoning with Uncertainty Chapter 10 - Learning with Uncertainty Chapter 11 - Causality Part IV - Planning and Acting with Uncertainty Chapter 12 - Planning with Uncertainty Chapter 13 - Reinforcement Learning Chapter 14 - Multiagent Systems Part V - Representing Individuals and Relations Chapter 15 - Individuals and Relations Chapter 16 - Knowledge Graphs and Ontologies Chapter 17 - Relational Learning and Probabilistic Reasoning Part VI - The Big Picture Chapter 18 - The Social Impact of Artificial Intelligence Chapter 19 - Retrospect and Prospect Appendix A - Mathematical Preliminaries and Notation Appendix B - Mapping to Open-Source Packages